Power Analysis#
Planning sample size so a test can detect an effect of interest.
Important
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What it is#
Power analysis plans a study around its statistical power — the probability of detecting a real effect (correctly rejecting H₀ when it’s false), which equals \(1 - \beta\). Most often it answers how many samples do I need?
The four levers#
Power, effect size, significance level α, and sample size are locked in a relationship — fix any three and the fourth is determined. Bigger effects, larger samples, or a looser α all raise power; the usual target is ≥ 0.80.
Why do it first#
Run before collecting data, power analysis prevents underpowered experiments that waste resources and are likely to miss true effects (a high Type II risk). Run after, it tells you how much you could realistically have detected — and warns against over-reading a non-significant result.
Theme: Statistical Inference & Power · All terminology
Hint
Mind map — connected ideas
Statistical Tests · Statistical Power · Chi-square (χ²) Test · A/B Testing · Confidence Intervals (CIs) · Kolmogorov–Smirnov (KS) Test
Hint
More in Statistical Inference & Power
A Priori Power Analysis · Chi-square (χ²) Test · Clopper–Pearson Interval · Compromise Power Analysis · Confidence Intervals (CIs) · Effect Size (δ) · Hypothesis Testing · Kolmogorov–Smirnov (KS) Test · Minimum Detectable Lift (MDL) · P-Value (probability value) · Post Hoc Power Analysis · Power (1 – β) · Sample size · Significance Level (α)
See also
Source article Adapted (context, re-expressed) in our own words from: Power Analysis (insightful-data-lab.com).